Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data

Abstract

Small-world networks are a class of networks that exhibit efficient long-distance communication and tightly interconnected local neighborhoods. In recent years, functional and structural brain networks have been examined using network theory-based methods, and consistently shown to have small-world properties. Moreover, some voxel-based brain networks exhibited properties of scale-free networks, a class of networks with mega-hubs. However, there are considerable inconsistencies across studies in the methods used and the results observed, particularly between region-based and voxel-based brain networks. We constructed functional brain networks at multiple resolutions using the same resting-state fMRI data, and compared various network metrics, degree distribution, and localization of nodes of interest. It was found that the networks with higher resolutions exhibited the properties of small-world networks more prominently. It was also found that voxel-based networks were more robust against network fragmentation compared to region-based networks. Although the degree distributions of all networks followed an exponentially truncated power law rather than true power law, the higher the resolution, the closer the distribution was to a power law. The voxel-based analyses also enhanced visualization of the results in the 3D brain space. It was found that nodes with high connectivity tended have high efficiency, a co-localization of properties that was not as consistently observed in the region-based networks. Our results demonstrate benefits of constructing the brain network at the finest scale the experiment will permit.

Modelling hierarchical structure in functional brain networks

“…We point out that this qualitative behaviour resembles the results obtained by Achard et al (2006) and Park et al (2008), where a slow decay for small degrees presents a crossover to an exponential-like decay for larger degrees. These results are in accordance with the extended analysis presented recently by Hayasaka & Laurienti (2010), who compared characteristics between region-and voxel-based networks in resting-state fMRI data, and also Zalesky et al (2009), who analysed brain networks at different ranges of scales, grey-matter parcellations and acquisition protocols. Their results show that comparison of networks across different studies must be made with reference to the spatial scale of parcellation.…”

Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness

“…Compared with complex networks, random networks are expected to have a lower average clustering. Characteristic path length L of a network is calculated as the harmonic mean of all geodesic distances d i,j between node i and j; it measures the average minimum number of steps that must be traversed to get from one node to another (Watts and Strogatz, 1998;Hayasaka and Laurienti, 2010). Path length has been associated to a network's global efficiency (Latora and Marchiori, 2001;Achard and Bullmore, 2007).…”

Graph analysis of functional brain networks: practical issues in translational neuroscience

“…Using single voxels directly as brain nodes was the earliest approach for graph analysis [21,22]. Proponents argue that the higher resolution afforded by this approach is a better representation of the real underlying system [23] and that it allows model-free analysis [24], but potential downsides include lower signal-to-noise ratio and increased graph size compared with aggregate-level analysis. Today, the most commonly used approach is to use a fixed anatomical atlas [19,25].…”